Data Mining and Knowledge Discovery in Metabolomics Armin

Data Mining and Knowledge Discovery in Metabolomics Armin

Christian Baumgartner (University for Health Sciences, Medical Informatics and Technology (UMIT), Austria) and Armin Graber (BIOCRATES Life Sciences GmbH, Austria)
DOI: 10.4018/978-1-59904-951-9.ch104
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Abstract

This chapter provides an overview of the knowledge discovery process in metabolomics, a young discipline in the life sciences arena. It introduces two emerging bioanalytical concepts for generating biomolecular information, followed by various data mining and information retrieval procedures such as feature selection, classification, clustering and biochemical interpretation of mined data, illustrated by real examples from preclinical and clinical studies. The authors trust that this chapter will provide an acceptable balance between bioanalytics background information, essential to understanding the complexity of data generation, and information on data mining principals, specific methods and processes, and biomedical application. Thus, this chapter is anticipated to appeal to those with a metabolomics background as well as to basic researchers within the data mining community who are interested in novel life science applications.

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